Passive control of, energy storage of NePCM, heat and mass transfer with gamma-shaped baffle in a thermo-bioconvection system using CFD and artificial intelligence

Abstract

The passive control of energy storage of Nano Enhanced Phase Change Material (NePCM), heat and mass transfer in a closed space was investigated using a Gamma-Shaped baffle in this numerical study. The Galerkin Finite Element Method was used to conduct a numerical study of the thermos-bioconvection system. The CFD data was used to calculate additional data using the Artificial Intelligence technique. The study was carried out for various parameters such as Darcy number, baffle position, Hartmann number, Lewis and Peclet numbers. The position of the L-shaped closed space is observed to be a control element for bioconvection heat transfer in a partially heated closed space, with an optimal value around omega = 150o. Artificial intelligence prediction algorithms, as they do in many other industries today, produce very useful results in the energy sector. In this study, we attempted to estimate the results of three different decision tree algorithms on the created datasets. In most of the datasets, we achieved 100% estimation accuracy using the single decision tree, random forest, and gradient boosted tree. We plotted a graph of the actual and predicted values for the dataset to compare the results. We believe that the successful outcomes will pave the way for exciting developments in the use of artificial intelligence prediction algorithms in the field of CFD.